Hands On Data Structure & Algorithm With Python: 2 In 1

Enhance you object-oriented skills with data structure & algorithms

What you'll learn

Set up your own development environment on Windows to create Python applications

Understand concepts such as divide and conquer and greedy and recursion algorithms in Python

Master dynamic programming and asymptotic analysis in Python for coding

Implement Stacks, queues/deques, hash tables, various algorithm such as BFS, DFS, Dijkstra's & and DAG Topological sorting

Use special Python techniques such as decorators and context managers

Requirements

Prior programming experience is assumed.

Description

Are you looking forward to get well versed with Python that is designed to ground you up from zero to hero in the shortest time? Then this is the perfect course for you.This course can be of utmost important to you as it guides you in many different ways such as learning basics of data structures, linked lists, and arrays along with coding tuples in Python followed by an example that shows how to program dicts and sets in Python. You will also be shown shown how to apply different algorithms such as Graph traversal, Shortest Path, Minimum Spanning Tree, Maximum Flow tree, and DAG topological sorting. It aslo demonstration on how to realize a hash table in Python.By end of this Learning Path,  you'll be well versed with Implementing Classic Data Structures and Algorithms Using Python along with building your own CV.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Learn Python in 3 Hours illustrates how u can be up-to-speed with Python in a short period of time, but your search has so far come up with disconnected, unrelated tutorials or guides.Learn Python in 3 hours is a fast-paced, action-packed course that maximizes your time; it's designed from the ground up to bring you from zero to hero in the shortest time. The course is based on many years of Python development experience in both large enterprises and nimble startups. In particular, the course's hands-on and practical approach comes from the author's experience in rapidly iterating and shipping products in a startup setting, where responsiveness and speed are key. With Learn Python in 3 hours, you will be up-and-running with Python like you are with your other languages, proving your value and expertise to your team today, and building your CV and skill set for tomorrow.The second course, Python Data Structures and Algorithms is about data structures and algorithms. We are going to implement problems in Python. You will start by learning the basics of data structures, linked lists, and arrays in Python. You will be shown how to code tuples in Python followed by an example that shows how to program dicts and sets in Python. You will learn about the use of pointers in Python. You will then explore linear data structures in Python such as stacks, queues, and hash tables. In these you will learn how to implement a stack and code queues and deques. There will also be a demonstration on how to realize a hash table in Python. Following this you will learn how to use tree/graph data structures including binary trees, heaps and priority queues in Python. You will program priority queues and red-black trees in Python with examples. Finally, you will be shown how to apply different algorithms such as Graph traversal, Shortest Path, Minimum Spanning Tree, Maximum Flow tree, and DAG topological sorting This course teaches all these concepts in a very practical hands-on approach without burdening you with lots of theory. By the end of the course, you will have learned how to implement various data structures and algorithms in Python. About the Authors:                                                                                                     Rudy Lai is the founder of Quant Copy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, Quant Copy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance—key analytics that all feed back into how our AI generated content. Prior to founding Quant Copy, Rudy ran High Dimension.IO, a machine learning consultancy, where he experienced firsthand the frustrations of outbound sales and prospecting. As a founding partner, he helped startups and enterprises with High Dimension.IO's Machine-Learning-as-a-Service, allowing them to scale up data expertise in the blink of an eye. In the first part of his career, Rudy spent 5+ years in quantitative trading at leading investment banks such as Morgan Stanley. This valuable experience allowed him to witness the power of data, but also the pitfalls of automation using data science and machine learning. Quantitative trading was also a great platform from which to learn a lot about reinforcement learning and supervised learning topics in a commercial setting. Rudy holds a Computer Science degree from Imperial College London, where he was part of the Dean's List, and received awards such as the Deutsche Bank Artificial Intelligence prize. Harish Garg, founder of BignumWorks Software LLP is a data scientist and a lead software developer with 17 years' software Industry experience. BignumWorks Software LLP is an India based Software Consultancy that provides consultancy services in the area of software development and technical training. Harish has worked for McAfee\Intel for 11+ years. He is an expert in creating Data visualizations using R, Python, and Web-based visualization libraries.Mithun Lakshmanaswamy, part of BignumWorks Software LLP, has been developing Applications in Python for more than nine years. He has written enterprise level distributed applications that are deployed on scores of servers and have the ability to support thousands of users simultaneously. Some of the applications he has developed are related to parsing millions of virus definitions, analyzing network packets from an enterprise setup, etc. He is also quite proficient in the teaching technical concepts and is quite involved with his current org’s training programmes. He has worked on multiple projects working with Python, AWS etc implementing the concepts of concurrent and distributed computing.

Overview

Section 1: Learn Python in 3 Hours

Lecture 1 The Course Overview

Lecture 2 Introducing Your One-Stop-Shop Python IDE – WinPython

Lecture 3 Writing Your First Hello World! Program in Python

Lecture 4 Using Functions, Lambdas, and List Comprehensions

Lecture 5 Downloading pip So That You Can Install New Packages

Lecture 6 Structuring Your Python Application with Classes and Modules

Lecture 7 Installing and Using pipenv to Manage Your Projects

Lecture 8 Object-Oriented Programming, the Pythonic Way

Lecture 9 Help Your Functions Do More Using Decorators

Lecture 10 Wrap Up All Dynamic Resources with Context Managers

Lecture 11 Create Your Own Crawlers with Scrapy

Lecture 12 Go Through News Articles with newspaper3k

Lecture 13 Digest RSS Feeds Using Feedparser

Lecture 14 Handle Your Big Datasets with NumPy and pandas

Lecture 15 Make Python Smarter with Machine Learning Using scikit-learn

Lecture 16 Visualizing Data in Charts and Graphs with matplotlib

Lecture 17 Generate a Static Website with Markdown and Pelican

Lecture 18 Customizing Your Static Website with Jinja2 Templates

Lecture 19 Deploying Your First Web Server with Flask

Section 2: Python Data Structures and Algorithms

Lecture 20 The Course Overview

Lecture 21 Introduction to Divide/Conquer

Lecture 22 Starting with Greedy

Lecture 23 Begin with Recursion

Lecture 24 Working with Dynamic Programming

Lecture 25 Using Asymptotic Analysis

Lecture 26 Examples of Linked Lists/Arrays in Python

Lecture 27 Coding Tuples, in Python Through Examples

Lecture 28 Programming Dicts in Python Through Examples

Lecture 29 Implementing Sets in Python

Lecture 30 Use of Pointers in Python Through Examples

Lecture 31 Examples on Stacks in Python

Lecture 32 Implementing a Stack in Python

Lecture 33 Coding for Queues in Python

Lecture 34 Utilizing a Deque in Python

Lecture 35 Realize a Hash Table in Python

Lecture 36 Basic Python Coding for Trees

Lecture 37 Implementing Binary Trees in Python Through Examples

Lecture 38 Examples of Heaps Queues in Python

Lecture 39 Programming Priority Queues in Python

Lecture 40 Coding Red-Black Trees in Python with Examples

Lecture 41 Working with Tries (or Search Trees) with Examples

Lecture 42 Python Coding for Graphs

Lecture 43 Directed Graphs

Lecture 44 Undirected Graphs

Lecture 45 Add Neighbor Function in Vertex Class

Lecture 46 Get Connections Function in Vertex Class

Lecture 47 Get Weight Function in Vertex Class

Lecture 48 Other Useful Graph Methods

Lecture 49 Breadth-First Graph Traversal Algorithm

Lecture 50 Depth-First Graph Traversal Algorithm

Lecture 51 Shortest Path Algorithm

Lecture 52 Implementing Shortest Path Through Dijkstra’s Algorithm

Lecture 53 Minimum Spanning Tree Algorithm

Lecture 54 Implementing Minimum Spanning Tree Through Kruskal’s Algorithm

Lecture 55 Coding Maximum Flow Tree Algorithm in Python

Lecture 56 Example on Programming Dag Topological Sorting

This course is for experience programmers who would like to transit into Python development while gaining hand-on practical skills in using data structures and algorithms with Python